PB1802: MUTATIONAL LANDSCAPE OF ACUTE MYELOID LEUKEMIA DEMONSTRATES INCREASED MUTATIONAL BURDEN WITH HIGH RISK MUTATION WITH AGING:INSIGHTS FROM SANGER’S NEXT-GENERATION AND WHOLE GENOME SEQUENCING ANALYSIS
Authors: Chrishthuka Kangatharan, Victor Ling, Michelle Richardson, Sheraz Bashir, Joanne King, Hafsana Siddique, Kirubah Selvaraj, Isobel Chalmers, Andrew Hodson, Mahesh Prahladan
Journal: HemaSphere (2023)

Topic: 3. Acute myeloid leukemia - Biology & Translational Research Background: Acute Myeloid Leukaemia (AML) is a heterogeneous disease with different mutational landscapes observed across different age groups. Next generation (NGS) and whole genome sequencing (WGS) have revolutionized the diagnosis and management of AML by allowing for simultaneous analysis of multiple genes, leading to more accurate risk stratification and personalized treatment strategies. This study aims to investigate the mutational landscape of AML in different age groups using NGS to provide insights into disease pathogenesis indicating clonal evolution Aims: This study aims to investigate the mutational landscape of AML in different age groups and explore the frequency and distribution of mutations across a panel of 41 genes using NGS. The study also aims to identify common and rare cytogenetic abnormalities associated with AML in different age groups and to assess their potential clinical and prognostic significance. Methods: We analyzed 51 patients with AML using next-generation sequencing (NGS) to identify mutations in a panel of 41 genes. Patients were divided into six age groups (below 30, 30-39, 40-49, 50-59, 60-69, and 70+) based on age at diagnosis. The mutational landscape of AML was analyzed for each age group. DNA is extracted from bone marrow samples obtained through bone marrow aspirate, then a NGS targeted TWIST panel captures exons of 75 Haem-Onc genes. Variants are identified using GATK and pindel, and only variants with likely or possible clinical relevance are reported. The assay has a limit of detection of 5% variant allele frequency and may not detect variants outside the targeted regions or large insertions/deletions, gene copy number alterations, or gene rearrangements. NGS involves fragmenting DNA, ligating adapters, and amplifying the DNA library for high-throughput sequencing. WGS sequences the entire genome and requires higher sequencing depth for accurate detection of genomic variants. Results: A total of 53 mutations were identified across the 41 genes analyzed, with the most common mutations found in the NPM1 gene. Different patterns of mutation frequency and distribution were observed across the age groups. Mutations in the NRAS, PTPN11, CEBPA, FLT3-ITD, RUNX1, WT1, FLTT3TKD, and NPM1 genes were observed in the below 30 age group. Mutations in the CEBPA, DNMT3A, IDH1, NPM1, and FLT3ITD genes were observed in the 30-39 age group. Mutations in the NPM1, STAG2, ASXL1, FLT3TKD, FLT3ITD, and DNMT3A genes were observed in the 40-49 age group. Mutations in the KIT, NF1, and RUNX1 genes were observed in the 50-59 age group. Various genes, including NPM1, NRAS, PTPN11, SRSF2, TET2, TP53, DNMT3A, CEBPA, ETV6, ASXL1, RUNX1, KRAS, IDH2, DDX41, ETNK1, SETBP1, U2AF1, CBL, IDH1, and PAX5 were observed in the 60-69 age group, while various genes, including ASXL1, IDH2, SRSF2, TP53, GNBI, KMT2C, RUNX1, STAG2, SF3B1, NPM1, CEBPA, DNMT3A, ETV6, FLT3ITD, TET2, IKZF1, MPL, NOTCH1, NF1, KRAS, and FLT3TKD were observed in the 70+ age group. Three patients in the 60-69 age group and two patients in the 70+ age group had no detected mutations based on the current AML NGS gene panel. Summary/Conclusion:Our study provides insights into the mutational landscape of AML across different age groups. The different patterns of mutation frequency and distribution observed in each age group have important risk stratification consequence which aids in treatment decision based on the performance status to have a better patient outcome.Keywords: Genomics, Mutation analysis, Mutation status, Acute myeloid leukemia